CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling

• URL: http://arxiv.org/abs/2510.12489v1
• Date: Tue, 14 Oct 2025 13:24:02 GMT
• Title: CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling
• Abstract summary: Time series anomaly detection plays a crucial role in a wide range of real-world applications.<n>Existing methods often model multi-scale information independently or rely on simple feature fusion strategies.<n>We propose CrossAD, a novel framework for time series Anomaly Detection that takes Cross-scale associations and Cross-window modeling into account.
• Score: 21.116017703252908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series anomaly detection plays a crucial role in a wide range of real-world applications. Given that time series data can exhibit different patterns at different sampling granularities, multi-scale modeling has proven beneficial for uncovering latent anomaly patterns that may not be apparent at a single scale. However, existing methods often model multi-scale information independently or rely on simple feature fusion strategies, neglecting the dynamic changes in cross-scale associations that occur during anomalies. Moreover, most approaches perform multi-scale modeling based on fixed sliding windows, which limits their ability to capture comprehensive contextual information. In this work, we propose CrossAD, a novel framework for time series Anomaly Detection that takes Cross-scale associations and Cross-window modeling into account. We propose a cross-scale reconstruction that reconstructs fine-grained series from coarser series, explicitly capturing cross-scale associations. Furthermore, we design a query library and incorporate global multi-scale context to overcome the limitations imposed by fixed window sizes. Extensive experiments conducted on multiple real-world datasets using nine evaluation metrics validate the effectiveness of CrossAD, demonstrating state-of-the-art performance in anomaly detection.

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